赵一帆

A passive imaging system for geometry measurement for the plasma arc welding process

Automatic and flexible geometry measurement of the weld pool surface can help better understand the complex welding processes and even provide feedback to better control this process. Most of existing imaging systems use an additional source of illumination to remove the light interference coming from the welding arc but it is usually costly. This paper introduces a novel low-cost optical-sensor-based monitoring system working under passive mode to monitor the wire + arc additive manufacture (WAAM) process, particularly for plasma arc welding. Initially, configurations and parameters of camera are investigated to achieve good visualisation of weld pool. A novel camera calibration methodology using the nozzle of a CNC machine is then proposed for this imaging system allowing estimation of the camera position with respect to the inspecting surface and its orientation in an easy-to use approach. The verification tests show that the average error of the calibration is less than 1 pixel. As a case study, an image analysis routine is proposed to measure the width of the bead during the welding process. The results show that the proposed system is effective to measure the dimension of weld pool.

A novel process-linked assembly failure model for adhesively bonded composite structures

The globally growing market for polymer composites and their increasing use within aircraft structures has necessitated reliable bonding of composite laminates to prevent structural failure. However, knowledge behind the interaction between curing process parameters and the failure of polymer composite bonded joints is not keeping pace with the market. A novel nonlinear correlation analysis has been employed and applied to experimental data, to attentively quantify the effect of curing parameters on the failure of bonded composite assemblies. The materials (adherends and adhesive) and the bonding processes were selected from those used in assembly of composite aircraft structures.

A novel defect depth measurement method based on nonlinear system identification for pulsed thermographic inspection

This paper introduces a new method to improve the reliability and confidence level of defect depth measurement based on pulsed thermographic inspection by addressing the over-fitting problem. Different with existing methods using a fixed model structure for all pixels, the proposed method adaptively detects the optimal model structure for each pixel thus targeting to achieve better model fitting while using less model terms. Results from numerical simulations and real experiments suggest that (a) the new method is able to measure defect depth more accurately without a pre-set model structure (error is usually within when SNR>32 dB) in comparison with existing methods, (b) the number of model terms should be 8 for signals with SNR∈ 8–10 for SNR>40 dB and 5–8 for SNR<30 dB, and (c) a data length with at least 100 data points and 2–3 times of the characteristic time usually produces the best results.

A compactness based saliency approach for leakages detection in fluorescein angiogram

This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7

Region-based saliency estimation for 3D shape analysis and understanding

The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. Secondly, a novel method is proposed for the estimation of the saliency value of each vertex. To this end, two new features are defined: Retinex-based Importance Feature (RIF) and Relative Normal Distance (RND). They are based on the human visual perception characteristics and surface geometry respectively. Since the vertex based method cannot guarantee that the detected salient regions are semantically continuous and complete, we propose to refine such values based on surface patches. The detected saliency is finally used to guide the existing techniques for mesh simplification, interest point detection, and overlapping point cloud registration. The comparative studies based on real data from three publicly accessible databases show that the proposed method usually outperforms five selected state of the art ones both qualitatively and quantitatively for saliency detection and 3D shape analysis and understanding.

New methods for onset detection of acoustic emission signals

Inferring the variation of climatic and glaciological contributions to West Greenland iceberg discharge in the twentieth century

Iceberg discharge is a major component of the mass balance of the Greenland Ice Sheet (GrIS). While bulk estimates of discharge variation over time exist, inferred remotely from measurements of grounding line ice velocities or surface mass balance calculations, few detailed measurements of discharge itself from individual marine-terminating glaciers existed until recent years. Recently, it has been shown, through a combination of ocean–iceberg modelling and non-linear system identification, that the century-long record of iceberg numbers crossing 48 oN in the West Atlantic is a good first-order proxy for discharge from at least south and west Greenland. Here, we explore the varying relative importance of ice sheet, oceanic and climatic forcing of iceberg discharge from these areas over the twentieth century, by carrying out sensitivity studies of a non-linear auto-regressive mathematical model of the 48 oN time series. We find that the relationships are mainly non-linear, with the contribution of the GrIS surface mass balance to iceberg discharge likely to be dominant in the first half of the century. This period is followed by several decades where oceanic temperature effects are most important in determining the model variation in iceberg discharge. In recent decades, all physical processes play a non-negligible part in explaining the iceberg discharge and the model suggests that the glacial response time to environmental changes may have decreased.

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods.

An edge detection method using outer totalistic cellular automata

A number of Cellular Automata (CA)-based edge detectors have been developed recently due to the simplicity of the model and the potential for simultaneous removal of different types of noise in the process of detection. This paper introduced a novel edge detector using Outer Totalistic Cellular Automata. Its performance has been compared with other recently developed CA-based edge detectors, in addition to some classic methods, through testing images from a public library. Visual and quantitative measurement of similarity with manually marked correct edges confirmed the superiority of the proposed method over conventional and state-of-the-art CA-based edge detectors

A coefficient clustering analysis for damage assessment of composites based on pulsed thermographic inspection

This paper introduces a coefficient clustering analysis method to detect and quantitatively measure damage occurring in composite materials using pulsed thermographic inspection. This method is based on fitting a low order polynomial model for temperature decay curves, which (a) provides an enhanced visual confirmation and size measurement of the damage, (b) provides the reference point for sound material for further damage depth measurement, (c) and reduces the burden in computational time. The performance of the proposed method is evaluated through a practical case study with carbon fibre reinforced polymer (CFRP) laminates which were subjected to a drop impact test with varying energy levels. A novel method for reducing an entire thermogram sequence into a single image is introduced, which provides an enhanced visualisation of the damage area.